“If you want to change the world, begin by making your bed. If you make your bed every morning you will have accomplished the first task of the day. It will give you a small sense of pride and will encourage you to do another task, followed by another, and another. By the end of the day that one task completed will turn into many tasks completed. Making your bed will also reinforce that the small things matter. If you can’t do the little things right, you’ll never be able to do the big things right.”
– William McRaven, Retired US Navy Admiral
These were inspiring words spoken by retired Admiral William McRaven as he addressed investors and Insight Partners’ team at our 2019 Annual General Meeting.
I asked myself the question: “How does this relate to software sales? What constitutes making your bed in scale-up sales software teams?”
My answer: data cleanliness and lean operations.
That’s right, the single most basic, most boring and often most overlooked element in sales is data cleanliness. Data cleanliness is at the core of how sales and finance leaders can radically scale their sales organizations, setting them up to be successful.
In the early stages of a SaaS company’s development, everyone is scrambling to do all sorts of sales functions. No one spends time, or has the time, to set up standard naming conventions or link the marketing and sales CRM systems with finance and customer service platforms. Many early stage companies don’t even have these standard systems – relying instead on excel or home-grown software. The challenge is that as companies scale, adding more customers and more sales reps, so too does the volume of data; more and more data piles up until it’s unmanageable. By the time companies have doubled and doubled again, they have many disconnected systems and no comprehensive view of the customer.
This can slow growth and result in shadow business intelligence/ analytics projects popping up throughout the organization to stitch together disconnected information for decision making. Organizational bloat has set in unless something is done about it. Companies with clean data, applying lean principles to decrease complexity have strong foundations to scale.
The fix for years of data neglect is not simple, but in the long run it enables better decisions to support significant growth. If your company is early in its scale up trajectory, you can address this before it becomes a problem.
Insight Partners recommends the following three steps:
1. Take out the trash – Get rid of old, unused or unclean data.
Sales teams love to believe that every element of data in their CRM is priceless, but that simply isn’t true. Data gets stale quickly as people move roles and companies, buying patterns change and companies themselves merge. In particular, "Account" and "Opportunity" data that’s more than a couple years old is likely no longer relevant and "Contact" data tends to age faster than a fruit fly. A simple rule of thumb is that if there is no activity or update of data in the past three years, then you need to consider either refreshing or dumping it. This will minimize the amount of data that needs to go through the rest of the processes (steps 2 and 3).
2. Map your data – select one source of truth to match other data to
In my experience there are a handful of “single sources of truth” throughout the company. These systems typically include the finance ERP (or at least the billing tool), the CRM, and the customer service system. Most people assume that the finance system is the cleanest of all platforms, which is true, although finance systems are not usually built around client relationships, and ongoing interactions with clients.
I worked at one company that had been highly acquisitive but hadn’t integrated their systems; they ended up with seven different finance systems each with its own client naming convention and no matching nomenclature between them. In that company, we were unable to answer the simple question of “who are our top 10 customers and which products do they use?
All systems have some way of naming the client that includes the address, phone number and email address. If you have those pieces, you can construct a Rosetta Stone to connect the 3-4 key systems.
Matching your client data between systems is like interpreting the Rosetta Stone and how you accomplish this match up depends on how large your customer database is.
- If you’re an early-stage company with a small customer set, then a manual stare-and-compare process might be suitable. Small in the enterprise context means less than 1000 accounts and typically less than 250 clients. In the Mid-market/ SMB context, it means less than 2500 accounts.
- Companies with slightly larger databases might try fuzzy matching tools. (Honestly, I’ve found mixed success with these, and the process still requires lots of manual work).
- Companies with large customer databases (e.g, 2500+ account records and more than 500 clients) should look at 3rd party tools such as Dun & Bradstreet, Infogroup, Zoominfo or Mintigo. These tools will compare addresses, names, URLs and other information available in your systems and match it to their data. Oftentimes, it’s possible to append the company identifier of the 3rd party’s data to your data file and use this to match across different datasets. One word of caution is to be careful to consider both parent company and child (subsidiaries/branches) or you’ll miss some of the matches.
3. Develop a Naming convention – establish a means of identifying your clients across all systems
Using client names sounds like a great way to start – it’s easy and everyone can get behind a name. The challenge with using names is that companies change names or acquire or spin off entities. And, human nature is to shorten things to make life easier when entering data. International Business Machines gets shortened to IBM in one system and I.B.M. in another system.
A better approach is to create an alphanumeric unique customer ID with 6 characters. This allows you to scale to millions of clients without duplicating numbers. Oh, and NEVER start with the number 0. Your excel nerds will thank you for that as they won’t need to convert to text before doing a look up (advice from a long-time excel nerd). If you can generate that unique ID from the sales system once a deal is labeled as closed-won and then pass it through to each of the other systems, while simultaneously feeding the data warehouse, you have an amazing solution that will keep your data clean for years to come.
So, what’s the benefit of this effort?
By taking the time to clean up your data, match up your different systems, and establish clear naming conventions, you now have a treasure trove of information that also includes firmographic information. Some of the projects that are now possible include:
- A model to determine the potential for upsell or cross-sell
- Ability to create balanced territories for sales reps
- Predictive analytics to determine when a customer might purchase or churn
- Ability to develop a full 360-degree view of the customer
I recently did this at a company with 30,000 prospects and roughly 6,000 clients. We leveraged D&B to match up our Salesforce extract and our finance extract. We were able to match 80% of companies using the automated tools and had to revert to a manual process for the remainder. In total, it took us four months to complete the process. The data proved invaluable. Following the initiative, we discovered that our supposed 6,000 clients were about 2,100 clients with multiple products and subsidiaries. This was eye-opening and enabled us to understand where to focus our time and resources. We also built our customer dashboards and greatly improved our territory modeling and quota setting.
In summary, one task completed – data cleanliness – leads to dozens of other tasks, that, at the end of the day, is invaluable to helping your company scale radically. So, come on everybody – make your bed!